Long-Tail Large Scale Face Recognition by Non-Linear Feature Level Domain Adaptation

1. A point of sale system with facial recognition, the point of sale system comprising: one or more cameras; a processor device and memory coupled to the processor device, the processing system programmed to: receive a plurality of images from the one or more cameras; extract, with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors from each of the plurality of images; generate, with a feature generator, discriminative feature vectors for each of the feature vectors; classify, with a fully connected classifier, an identity from the discriminative feature vectors; and control an operation of the point of sale system to react in accordance with the identity.

2. The point of sale system as recited in claim 1 , further includes a communication system.

3. The point of sale system as recited in claim 1 , wherein the operation logs a customer into the point of sale system and greets the customer.

4. The point of sale system as recited in claim 1 , wherein the operation logs an employee into the point of sale system and greets the employee.

5. The point of sale system as recited in claim 1 , wherein the operation recognizes a customer and permits a purchase without an employee intervention.

6. The point of sale system as recited in claim 1 , wherein the one or more cameras is a ceiling mounted security camera.

7. The point of sale system as recited in claim 1 , further programmed to train the feature extractor, the feature generator, and the fully connected classifier with an alternative bi-stage strategy.

8. The point of sale system as recited in claim 1 , wherein the feature extractor shares covariance matrices across all classes to transfer intra-class variance from regular classes to the long-tail classes.

9. The point of sale system as recited in claim 1 , wherein the feature generator optimizes a softmax loss by joint regularization of weights and features through a magnitude of an inner product of the weights and features.

10. The point of sale system as recited in claim 1 , wherein the feature extractor averages the feature vector with a flipped feature vector, the flipped feature vector being generated from a horizontally flipped frame from one of the plurality of images.

11. The point of sale system as recited in claim 1 , wherein each of the plurality of images is selected from the group consisting of an image, a video, and a frame from the video.

12. The point of sale system as recited in claim 2 , wherein the communication system connects to a remote server that includes a facial recognition network.

13. The point of sale system as recited in claim 7 , wherein one stage of the alternative bi-stage strategy fixes the feature extractor and applies the feature generator to generate new transferred features that are more diverse and violate a decision boundary.

14. The point of sale system as recited in claim 7 , wherein one stage of the alternative bi-stage strategy fixes the fully connected classifier and updates the feature extractor and the feature generator.

15. A computer program product for a point of sale system with facial recognition, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: receiving, by a processor device, a plurality of images; extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images; generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors; classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector; and controlling an operation of the point of sale system to react in accordance with the identity.

16. A computer-implemented method for facial recognition in a point of sale system, the method comprising: receiving, by a processor device, a plurality of images; extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images; generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors; classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector; and controlling an operation of the point of sale system to react in accordance with the identity.

17. The computer-implemented method as recited in claim 16 , wherein controlling includes recognizing a customer and permitting a purchase without an employee intervention.

18. The computer-implemented method as recited in claim 16 , wherein controlling includes logging a customer into the point of sale system and greeting the customer.

19. The computer-implemented method as recited in claim 16 , wherein controlling includes logging an employee into the point of sale system and greeting the employee.